Specialty pharmacy’s true out-of-pocket costs

At what price will a member decide to forgo initiating a specialty pharmaceutical because the out-of-pocket (OOP) cost will cause financial hardship? Prime Therapeutics wanted to find out if there was an association between OOP expenses and abandonment of new prescriptions for tumor necrosis factor (TNF) blockers and multiple sclerosis (MS) specialty drugs.

Patrick P. Gleason, PharmD, director of clinical outcomes at Prime Therapeutics and lead author of the study, says that for members prescribed a TNF blocker, the odds of abandonment (the prescription has been filled, the claim adjudicated and the medication is ready to be dispensed, but the member does not receive or refuses the medication, probably because of the cost) were 6-times higher at an OOP cost greater than $500, compared with OOP costs of less than $100.

Gleason says that “for members filling [a prescription for] an MS medication, if the OOP was more than $200, 1 in 4 were found to have abandoned the drug. If the OOP was less than $100, only 1 in 20 would abandon the drug.”

The researchers studied 6,123 members of a managed care organization who were receiving newly initiated TNF blocker therapy. Nearly 84 percent had a per-claim OOP expense of $0–$100. There were 2,303 members who were newly initiating MS self-injection therapy, 83 percent of whom had a per-claim OOP expense of $0–100.

“Our research suggests that about $200 is the point at which members start thinking of not accepting or not paying for their medication,” says Gleason.

The researchers say that these results “establish potential break points, at which OOP expenses may negatively influence medication utilization, of greater than $100 for TNF blockers and greater than $200 for MS medications.”

Rate of abandonment by out-of-pocket member expense*

The data points for each graph represent 95% confidence error intervals. Because the population within each cost share group is small (with the exception of the $0 to $100 group), the confidence intervals are large. Mathematically, the data point could lie anywhere along the interval.